Wireless data communications devices, systems and networks that are in widespread use worldwide have become sophisticated and complex, due to the increasing need for higher data rates and the support of an increased number of users and data traffic. Accomplishing these higher rates and traffic capacities usually requires employing complex signal waveforms and advanced radio frequency capabilities such as multiple-input multiple-output (MIMO) signal coding, transmit and receive signal management methods such as beamforming, and spatial multiplexing techniques. MIMO coding in particular has received significant recent interest, as it employs the statistical properties of RF propagation channels to achieve higher data rates as well as to simultaneously accommodate multiple users (spatial multiplexing). All of these techniques, however, increase the complexity of the wireless devices. Manufacturers, vendors and users therefore have a greater need for better testing of such systems.
Unfortunately, the increasing complexity of wireless data communication devices and systems also makes them harder to test. Testing MIMO wireless systems is particularly problematic due to the difficulty of re-creating the dynamic RF channel environment. Actual open-air RF environments contain high levels of uncontrollable noise and interference, and also present time-varying and unpredictable channel statistics. However, the performance of MIMO systems is very dependent on the channel statistics. The lack of controllability and repeatability also makes it difficult or impossible to automate the testing of such wireless systems. Therefore it is very attractive to manufacturers and users to test these devices in a repeatable fashion by excluding the variability of real MIMO RF channels while still interposing accurately simulated but controllable channels. This also enables the tests to be conducted in an automated fashion.
With reference to FIG. 1, an exemplary MIMO wireless transmitter 101 and an exemplary MIMO wireless receiver 102 is shown in a simplified RF propagation environment, that may consist of an arbitrary number of RF scatterers 107. MIMO transmitter 101 has a plurality of antennas 103. Similarly, MIMO receiver 102 has a plurality of antennas 104. As depicted in FIG. 1, the multiplicity of antennas enable the transmission of multiple parallel streams of information 106, utilizing the available transmission paths (or ‘modes’) in the RF environment, which are created by the presence of scatterers 107. It is apparent that the performance gains due to MIMO occur as a consequence of these multiple transmission modes; removal of the scatterers causes the multiple transmission modes to collapse into a single mode, and the channel will then become unable to support more than one stream of information. Therefore, any MIMO test system must provide a means of supporting multiple transmission modes in the path between the transmitter and the receiver.
FIG. 2 is illustrative of a simplified MIMO wireless traffic and radio analyzer 111 that may be coupled to a wireless device under test (DUT) 110 containing MIMO radio interface 112 by RF cables 113. In this case, the multiple transmission modes of the real RF propagation channel may be simulated by the multiple separate cables 113, which may interconnect RF transmitters and receivers in pairs. The number of independent transmission modes (and therefore the number of parallel data streams) is equal to the number of distinct RF cables and associated transmitter/receiver pairs. All external interference, noise and propagation variations are excluded by virtue of the use of such a fully cabled RF setup.
For representational purposes, FIG. 2 and all succeeding figures herein show three cables, antennas, transmission paths, modes, etc. However, it should be understood that this is done for representational convenience, and the actual number thereof may be any number including 1. It is also not necessary for the numbers of transmitters, receivers, cables, antennas, transmission paths, modes, etc. to be equal to each other. The discussion and teachings herein are equally applicable to a MIMO system comprising any number of transmission paths and antennas and any other number of reception paths and antennas.
The exemplary system depicted in FIG. 2 shows an idealized (nearly lossless, noiseless and distortion-free) MIMO RF channel between analyzer 111 and DUT 110. In practice, however, RF propagation channels are neither lossless nor distortion-free. Turning now to FIG. 3, the loss and amplitude/phase distortion presented by actual RF propagation channels may be simulated by channel simulator (fader) 120, which is interposed between analyzer 111 and DUT 110. Such a channel simulator 120 is connected to analyzer 111 by RF cables 113, and to DUT 110 by RF cables 121, and therefore the system continues to exclude external interference and avoid uncontrollable propagation variations. However, the propagation characteristics of actual RF channels can be simulated in a controlled and repeatable fashion by modifying the configuration of channel simulator 120. The design of such a channel simulator 120 is well known in the art and will not be repeated here.
FIG. 4 depicts a situation where a single MIMO receiver 130 may receive signals concurrently from a plurality of MIMO transmitters 131, 132, 133. With a sufficiently large number of scatterers 135 in the RF propagation environment, it is possible for completely independent transmission paths (i.e., propagation modes) to be present between each of the MIMO transmitters 131, 132, 133 with respect to MIMO receiver 130. By applying appropriate digital signal processing (DSP) functions, it is possible for MIMO receiver 130 to distinguish and separate the transmitted signals from each other by virtue of these independent propagation modes. It may therefore be possible for multiple users to concurrently transmit RF signals within the same frequency band to the same receiver. This is a form of spatial multiplexing referred to as multi-user MIMO (MU-MIMO). It should be noted that the statistical properties of the RF propagation channels between the transmitters and the receiver are even more important for MU-MIMO, as the parallel streams of information are disambiguated and extracted solely by virtue of their having traversed different RF paths and having been subjected to different amplitude/phase distortions.
It will be appreciated that the situation in FIG. 4 may equally apply to a single MIMO transmitter concurrently transmitting data streams to a plurality of MIMO receivers. In this case, the transmitter may accept parallel streams of information destined for separate receivers, apply different signal processing functions to the data streams, and combine these streams for transmission on a single set of antennas. The signal processing functions are selected in such a way as to employ the statistical properties of the different RF channels existing between the transmitter and the various receivers, and maximize the desired signal at each receiver while minimizing the undesired signals (i.e., those destined for other receivers).
To enable distinct RF propagation channels to concurrently support separate MU-MIMO data streams, it may be essential that the characteristics of each individual RF propagation channel be accurately determined. This is normally performed by a process referred to as sounding the channel. Sounding entails transmitting a known signal with precisely defined properties from each transmitter to each associated receiver, and then measuring the received signal at the receiver. The RF channel between the transmitter and the receiver can then be estimated by comparing the received signal with the predetermined transmitted signal. The receiver may then feed the measured RF channel properties back to the transmitter using a predetermined control protocol. The transmitter uses these channel properties to adapt subsequently transmitted signals to the RF channel between itself and the receiver, thereby ensuring that the reception probability is maximized at the target receiver and minimized everywhere else.
With reference to FIG. 5, a possible arrangement for testing a MU-MIMO DUT 147 containing MU-MIMO radio interface 148 is depicted. In this case, wireless radio and traffic analyzers 141, 142, 143 may simulate a plurality of spatially distributed end-stations, generating independent streams of wireless traffic to DUT 147. As MU-MIMO relies upon the existence of different RF propagation channels between transmitter/receiver pairs, separate channel simulators 144, 145, 146 may be employed, one for each of analyzers 141, 142, 143. Each channel simulator may be configured to simulate a different radio channel. The outputs of channel simulators 144, 145, 146 may be combined together via RF power combiners 149 and fed to MIMO radio interface 148 in DUT 147.
Such an arrangement, unfortunately, suffers from several significant shortcomings. Firstly, the use of separate channel simulators 144, 145, 146 causes such a system to become prohibitively expensive. This is particularly true as the number of end stations represented by analyzers 141, 142, 143 increases to a large number (e.g., 500). Secondly, coupling together multiple channel simulators 144, 145, 146 causes them to interact in unpredictable ways, considerably degrading the effectiveness of the simulated RF channels, and often causing substantial distortion effects. Finally, such a system presents significant issues in terms of signal dynamic range, particularly as the number of channel simulators increases; a high-amplitude signal produced by one channel simulator may overload another channel simulator which may be producing a low amplitude signal. For these reasons, simply attaching together multiple channel simulators 144, 145, 146 to create an MU-MIMO test system is not feasible except for certain limited and carefully selected cases.
To comprehend the general functioning of an MU-MIMO system, the operation of a simple MIMO system (i.e., a single MIMO transmitter and a single MIMO receiver) will be considered first. With reference to FIG. 6, an exemplary MIMO transmitter 150 that may incorporate one method of beamforming is depicted, using one or more antennas 157 to transmit RF signals over some RF propagation medium to one or more antennas 161 of an exemplary MIMO receiver 160.
MIMO transmitter 150 may include: transmit digital data input 151, digital modulator 152 that may transform digital data to the modulation domain, for example by employing Orthogonal Frequency Division Multiplexing (OFDM); space-time mapper 153 that may map modulated symbols to one or more output streams of symbols according to some MIMO mapping algorithm; transmit precoder 154 that may perform some transformation upon the symbol streams to adapt them for transmission; digital to analog (D/A) converters 155 that may convert the digital representation of the transformed symbols to analog; and transmit RF processing functions 156 that may convert these analog signals to some desired radio frequency and transmit them using one or more antennas 157. It is understood that other functions and processing elements may also be included in MIMO transmitter 150, but are not relevant to this discussion and are therefore omitted.
MIMO receiver 160 may receive transmitted RF signals from one or more antennas 161, and may include: receive RF processing functions 162 that convert one or more streams of RF signals, after which analog to digital (A/D) conversion by A/D converters 163 may be performed to produce digital symbols; receive decoder 164 that may transform the streams of digital symbols prior to demapping and demodulation; and space-time demapper and digital demodulator 165 that may map and integrate one or more streams of symbols according to a predetermined space-time transformation, and may demodulate these symbols to recover received digital data 166. Channel estimator 167 may calculate the properties of the RF propagation medium that may exist between transmit antennas 157 and receive antennas 161, and supply this information to receive decoder 164 and space-time demapper and digital demodulator 165, to aid in transforming and recovering the digital data 166. It is likewise understood that other functions and processing elements may be included in MIMO receiver 160 but are omitted as they are not relevant to this discussion.
The properties of the RF propagation medium influence the efficiency with which MIMO signals can be transmitted and received. The RF channel properties may be used to derive the coefficients that may be set into transmit precoder 154 to adapt the symbol streams generated by space-time mapper 153 to the propagation modes of the RF channel, which may maximize the information density of the channel. Such an adaptation may be commonly referred to as beamforming or, more specifically, eigen beamforming. The RF channel properties may further be used to calculate coefficients that may be set into receive decoder 164 to post-process the received symbol streams from the propagation modes of the RF channel, which may thereby enhance the signal-to-noise ratio at MIMO receiver 160 (indirectly further maximizing the information density of the channel). Such an enhancement may be commonly referred to as combining diversity.
It is therefore apparent that an accurate knowledge of the properties of the RF channel, in particular its propagation modes, may be of great importance. It is also apparent that the receiver and transmitter may preferably share the properties of the RF channel, so that the processing performed at the transmitter corresponds to the processing performed at the receiver. Therefore, MIMO receiver 160 may preferably share channel information with MIMO transmitter 150 to achieve this goal, further preferably using a known and well defined protocol. Such a protocol for determining and sharing channel state information is commonly known as a beamforming information exchange process.
Turning now to FIG. 7, an exemplary procedure is depicted that may be used for determining the properties of the RF channel, for communicating these properties between the two ends of an RF link, and for utilizing these properties in the transmission and reception of data. Vertical lines 170 and 172 represent the operations of a MIMO transmitter and a MIMO receiver respectively. At 172, the MIMO transmitter may generate some fixed test data having a prearranged bit pattern and predetermined modulation and spatial mapping characteristics, and may transmit this data as a sounding signal, for example within a sounding packet, as represented at 173. At 174, the MIMO receiver may receive and analyze the sounding signal, which may be a sounding packet. The original sounding signal waveform being known, at 175 the MIMO receiver may calculate the RF channel properties by their effect upon the sounding signal waveform, and may further compute a precoding matrix (that may, for instance, be used within exemplary MIMO transmitter 150) that maximizes the information density for the RF channel existing between the MIMO transmitter and receiver at that point in time and space. At 176, the coefficients of the precoding matrix may be formatted into suitable packet(s) and transmitted at 177 as a beamforming information frame, completing the beamforming information exchange process. This beamforming information exchange process may sometimes also be referred to as a beamforming training sequence.
At 178, the MIMO transmitter may extract the coefficients of the precoding matrix that have been provided by the receiver and process them to obtain the actual configuration of the precoder, which may then be applied to the transmit precoder at 179. Once the transmit precoder has been configured, the transmitter may subsequently send user data frames; these frames may be processed by the transmit precoder to adapt them to the RF channel and transmitted as precoded signals 180. Such a process may maximize the signal to noise and interference ratio (SINR) at the MIMO receiver and may further enable optimal reception of the user data frames. (It is understood that the MIMO receiver may also utilize the RF channel properties to configure a receive decoder and receive demodulator, as is depicted in FIG. 6 and may yet further improve the SINR.)
FIG. 8 shows a simplified exemplary mathematical model of the process of precoding, transmission through a MIMO RF channel, and decoding. With respect to FIG. 8, vectors [x] and [y] represent complex-valued transmitted and received information signals respectively; complex vectors [V] and [U] may represent transmit precoding matrix and receive decoder matrix coefficients, respectively; and the RF channel existing between the MIMO transmitter and MIMO receiver is represented by [H]. At 200, the user data stream is input as a sequence of vectors [x]. In transmit precoder 201, the vectors are multiplied by the transmit precoding matrix [V], after which they are transmitted upon RF channel 202. The effect of the RF channel 202 upon the transmitted signal is represented by a multiplication by the channel matrix [H]. These signals are received by receive decoder 203 and multiplied further by receive decoder matrix [U], yielding a sequence of vectors [y] that comprise the received data. Note that this depiction is highly simplified for the purposes of explanation and does not include such elements as modulation, demodulation, spatial mapping, spatial demapping, coding, etc. that are not germane to this discussion. Also note that this is a simplified model and does not take into account operations such as vector transposes that may actually be required for the vectors [U] and [V].
The beamforming information exchange process may attempt to determine the coefficients of vectors [V] and [U] that will maximize the SINR of the signal transmitted through channel matrix [H]. An optimal beamforming information exchange process may calculate these vectors in such a way that, barring the effects of noise, the signal [y] matches the signal [x]; i.e., the effect of RF channel matrix [H] is nullified.
With regards to FIG. 9, an exemplary mathematical model of an MU-MIMO process is depicted. Note that the model may be applied to any number of transmitters and any number of receivers. It may be observed that the steps are substantially similar to that of the basic MIMO process shown in FIG. 8. Input signal vectors 210, 215, 220 corresponding to vectors [x1], [x2], [x3] respectively may be precoded by transmit precoders 211, 216, 221 with transmit precoding matrices [V1], [V2], [V3], after which they may transmitted over RF channels 212, 217, 222 with different channel matrices [H1], [H2], [H3] respectively. A distinguishing feature of MU-MIMO is that all of the signals are transmitted concurrently and share the same spatial environment; therefore, at 225 the transmitted signals are shown as being arithmetically combined, so that the same signals are effectively received at all sets of receive antennas. These signals may then be processed by receive decoders 213, 218, 223 with receive decoder matrices [U1], [U2], [U3] respectively, which may yield at 214, 219, 224 the output signal vectors [y1], [y2], [y3]. Each transmit precoding matrix and each receive decoder matrix may preferably be adapted to the specific RF channel matrix existing between that transmitter/receiver pair. For instance, transmit precoding matrix [V1] and receive decoder matrix [U1] may be adapted to RF channel matrix [H1], which may ensure optimal decoding of signal [y1], and may further enable the RF signals generated by the other transmitter chains to be rejected. Separate channel estimation and beamforming feedback processes may hence be employed for each transmitter/receiver pair. As depicted in FIG. 10, channel estimation function 230 may process the signal received as [y1], and beamforming feedback function 234 may then pass the coefficients that may be used by transmit precoder 211 to the corresponding transmitter. Similarly, channel estimation functions 231, 232 and beamforming feedback functions 235, 236 may perform similar actions for other signal chains.
It is known that if orthogonal channel matrices [H1], [H2], [H3] exist between different transmitter/receiver pairs [V1]/[U1], [V2]/[U2], [V3]/[U3] respectively, then orthogonal transmission modes exist between each transmitter/receiver pair. The transmit precoding matrices may be adjusted to utilize these orthogonal transmission modes. Further, the receive decoder matrices may be adapted to perform diversity reception within these orthogonal transmission modes. This may have the effect of raising the SINR of the desired signals while reducing the SINR of the undesired signals. It is further known that such an arrangement may enable simultaneous transmission and reception of independent signals [x1], [x2], [x3] over the same RF channel, which is the essence of MU-MIMO.
It is understood that the transmitter chains shown in FIG. 10 may be combined into a single device, while the receiver chains may be present in separate devices. Alternatively, the transmitter chains may be in separate devices, while the receiver chain may be combined into one device. (This latter situation is represented in FIG. 4.) Normal MU-MIMO usage situations entail one or the other of these cases. It is not of significant interest to consider the case of fully independent transmitter chains and fully independent receiver chains, as these degenerate to the standard MIMO usage situation.
It is apparent that an MU-MIMO system requires an RF channel with a multiplicity of orthogonal transmission modes between the different transmitter/receiver pairs, so that the transmit precoders and receive decoders can be adjusted to enhance the desired signals while suppressing undesired signals and noise. However, this situation is not obtained in a fully cabled environment. With reference to FIG. 11, a single MIMO transmitter/receiver pair is depicted, which may be equivalent to the MIMO transmitter/receiver pair shown in FIG. 8 with the exception that the antennas and the open-air MIMO RF transmission channel have been replaced by RF cables 241. As these cables may be nearly lossless and free of reflections, they may represent a channel matrix [Hc] as shown at 240, which is an identity matrix. This may still be a valid MIMO environment for a single transmitter/receiver pair, and may still enable transmit signal [x] at 200 to be transmitted through the system and received as signal [y] at 204. Therefore, a MIMO system may still continue to function properly when cable-connected instead of using propagation through an actual RF environment.
Turning now to FIG. 12, a possible mathematical model of the MU-MIMO situation in a cabled environment is shown. This may comprise one or more transmitted signal streams 210, 215, 220 that may be precoded by transmit precoders 252, 253, 254 which implement the [V1], [V2], [V3] transmit precoding matrices respectively. The signals may then be passed through unitary RF channels 240, 241, 242, all of which have the identical RF transmission channel matrix [Hc] created by cables 243. They may then be subsequently combined and distributed to receive decoders 213, 218, 223 as before, which may implement the [U1], [U2], [U3] matrices respectively. The output signals [y1], [y2], [y3] (214, 219, 224 respectively) may contain the decoded received data, which may also be fed to channel estimation functions 230, 231, 232, the outputs of which in turn may be fed to beamforming feedback 234, 235, 236 and subsequently used to configure transmit precoders 252, 253, 254.
It will be observed that in a cabled environment RF channel matrices [Hc] between every pair of transmitter/receiver chains are identical, and are equal to the identity matrix. Further, channel estimation functions 230, 231, 232 will produce identical channel estimates, and hence the coefficients configured into transmit precoders 211, 216, 221 will be the same, as will the coefficients for receive decoders 213, 218, 223. As MU-MIMO relies for its operation on orthogonal RF channels creating orthogonal transmission modes, it is readily apparent that such a system cannot support simultaneous transmission and reception of independent signals. In the cabled situation depicted, therefore, the capacity of the RF transmission channel collapses to that of the simple MIMO case, and testing of MU-MIMO operation is not possible.
The known methods of MU-MIMO wireless testing therefore suffers from serious shortcomings. There is hence a need for improved MU-MIMO wireless data communication test systems and methods. A test system that is capable of performing tests upon MU-MIMO systems in a cabled environment may be desirable. It may be preferable for such a test system to eliminate the need for external channel simulators to enable the testing of multiple simultaneous transmitters or receivers at reduced cost. Further, such a test system may preferably permit different RF channels to be simulated for different transmitters or receivers without interaction between the channels. Finally, it may also be desirable for the test system to facilitate the testing of large-scale MU-MIMO systems with many transmitters and receivers.